In experiments, we use three benchmark datasets that are miniImageNet[1]}, tieredImageNet [2]}, and FC100[3]}. In miniImageNet dataset from ILSVRC-12 [4]}, RGB images include 100 different classes, and each class has 600 samples. We adopt the splits configuration [5]} that respectively is 64,16,and 20 classes for training, validation and testing. In tieredImageNet dataset from ILSVRC-12 [4]}, there are more than \(700k\)  images from 608 classes. Moreover, 608 classes is collected for 34 higher-level semantic classes, each of which has 10 to 20 classes. We also use the splits configuration [5]} that respectively is 351,97, and 160 for training, validation and testing. Each class has about 1281 images. In FC100 dataset from CIFAR-100[8]}, there are 100 classes images grouped into 20 higher-level classes. Classes respectively are divided into 60,20, and 20 for training, validation and testing. Each classes have 600 images of size \(32\times 32\) . Table REF  shows the statistics information of these datasets.
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